Bibliographic record
Abstract
With the objective of tax-minimization towards increasing shareholder return, tax-avoidance strategies are being frequently employed by corporations especially multinationals. In the wake of increasing evidence of corporate collapses linked with tax-avoidance maneuvers, both economic and legal experts have revisited the role of anti-avoidance rules as a measure to promote corporate tax-compliance. While few countries are satiated with the judicially enforced doctrines countering tax-avoidance, the recent trend is to legislature the anti-avoidance rules for empowering tax-administrations to challenge abusive and tax-motivated transactions. The case of India is no different which has introduced general anti-avoidance rules or ‘GAAR’ in the direct tax law permitting the tax-administration to counter ‘impermissible avoidance arrangements.’In the backdrop of these changes the paper makes a survey of economic and legal literature on the positioning of GAAR in tax legislations and its impact on the corporate governance standards. The idea is to present a holistic understanding of the numerous variables to explore the intertwined relationship between tax-avoidance and corporate governance and thus examine the impact of GAAR upon the prevailing corporate culture of adopting aggressive tax-positions. The success of GAAR in other jurisdictions which have fair bit of experience of their application is also examined so as to draw lessons for corporate managers. The paper examines the economic factors applicable to measure the relationship between GAAR and corporate governance but the appraisal of this relationship is undertaken predominantly in a legalistic backdrop where the idea is to draw lessons from a theoretical as well as pragmatic perspective on how GAAR abet changes in corporate decision-making styles vis-a-vis the tax considerations at play. Upon an assessment of the competing considerations and drawing lessons from Australia, Canada, New Zealand and United Kingdom in particular, the paper concludes that while GAAR will indeed act as a deterrent against opportunist tax-behaviour but may not serve as a statutory corporate governance rule as many would expect it to be.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".